1. Field of the Invention
The present invention generally relates to organization and retrieval of stored digital content (such as, but not limited to, records in database, documents, web pages, merchandise, people contact information, to-do, task, book information, audio files, video files, emails, instant messages, maps, graphic/picture files, photo files, folders, machine codes or machine scripts, an application specific files, application scripts, etc.) and human motives (such as, but not limited to, thoughts, meanings, preferences, priorities, objectives, goals, project structures, planning ideas, intentions, wishes, action items, decisions, conclusions, moods, motivations, sequences, questions, etc ), e.g., on personal computers, on computer networks, on the web, on central databases, in the notebook, etc. The present invention also generally relates to integration of human motives with the stored digital content. The present invention also generally relates to processes that enable systematical and easy update stored information organization to cope with the fast evolving and diversified human motives and digital content collection. The present invention also generally relates to processes that enable viewing and retrieving the organization of stored information from different perspectives that driven by user's circumstantial needs. More particularly, the present invention is related to a crosslink data structure, crosslink database, and systems and methods of organizing and retrieving digital content and human motives.
2. Description of Related Art
There are five basic platforms that we generally used in the prior art to retrieve and/or store information (that includes digital content and motives). Each platform has its strengths in meeting certain basic needs of storing and retrieving information:                1. Search engine: desktop and internet: Google, Yahoo, MSN, Ask Jeeves, Corpenic, etc.        2. Tree structure and tagging: Microsoft folder structure, organization chart, build of materials, merchandise lookup for tree structure and tagging for Flikr.com and del.icio.us.        3. Hyperlink: Web pages, MS documents, PDF documents.        4. Conventional database: Email tools, files in a folder, customer records in SQL server, etc.        5. Chronicle notebooks/diary: Emails, hard bound paper notebook, etc.        
To accomplish various objectives, users need to integrate their motives (projects/thoughts/objectives/tasks/do-to, etc.) with their digital content. Because of the use of various platforms, users need to hop between these platforms to access various pieces of information. It is very difficult to efficiently manage this task because the digital content and the motives tend to be unstructured, fast changing, out of synchronization and scattered over time. Currently most people do this integration function in their heads. Additionally, the increasing demands in efficiency and effectiveness of processing large volume of information in the Internet era have become the root cause of information overload for individuals at work and at home. Therefore, a solution is needed to help users integrate the stored digital data with individual motives.
The examples of basic human needs for efficient integration and process of motives with the stored digital content are as follows:                1. Find specific, unique digital content in the mass volume of information: When the information is specific, unique, and unexpected for its purpose, the ability to search is critical. The search engine is very useful in this aspect.        2. Personalize digital content to fit personal perspective: Each person views information from a different perspective and value.        3. Organize unstructured information that arrives at the wrong sequence: The daily information that people encounter is often random, disorganized, and arriving at the wrong sequence. It is often broad and versatile. It is difficult to organize information under such circumstances.        4. Show interrelationship between information: The same piece of information can be useful for many different motives and can relate to other information. It is a daunting job to manage the complex, cross-related information while the collection of digital content and human motives constantly evolve.        5. Relate information to time: Many motives are more urgent than others. More recent information is more likely to be more relevant and need to be taken with a higher priority than an old piece of information. Sometimes people remember information by time, but cannot remember the specific subjects. Sometimes people remember things to follow up by reviewing notebooks but otherwise can not remember.        6. Integrate information into a big picture: When information relates and integrates, we gain better perspective and vision of our motives. It is also easier to identify the right information if we have a clear picture of what is available from a bigger scope surrounding our scope of interests.        7. Give dynamic perspective view of information based on the needs of specific motives or digital content: The chance of being right in the early beginning is small because the collection of digital content and motives often evolve constantly, rapidly, and unpredictably. But the prior art system requires us to fix the perspective based on current available digital content and motives.        8. Handle different types of information: In the Internet era, we often need to combine digital content from the web, emails, documents, notebook, etc with our motives so we can perceive or act effectively and efficiently. Today we often record motives separately from the organization of information and we rely on our memory to cross-relate them. But our memory is not reliable and we are neither efficient nor effective in drawing the complex relationship.        9. Retrieve information by impression: Since our memory is often not reliable, we need to be reminded what is available. Sometimes we may not recall the intended topic but can trace it from other related topics. Sometimes we may remember only the major topic and cannot recall the details of sub topics.        10. Easily update information and its structure: Information and structure needs to be constantly up dated to be meaningful because information may evolve quickly and unpredictably. Our collection of digital content and our motives form a close feedback loop (influential of each other). We acquire digital content based on our motives whereas our motives could alter by what we learn from the collection of digital content. This makes information data structure unpredictable. The solution is not to acquire the fortune telling ability but the ability to easily revise information and information data structure.        
A comparison chart of how each platform's strength in meeting these needs is show in FIG. 44. It points out that how each platform affectively meets part of these requirements but none of them meets all the requirements. It is one of the invention's objectives to integrate these platforms by providing an interface between the users and these platforms.
Personal Computers—File Folders
Computers generally interface to one or more storage devices, including for example, removable or non-removable storage media such as floppy disks, hard drives, CD ROMs, digital versatile or video disks (DVD) and the like. These storage media may be local or on a network accessed by the computer, and used to store and retrieve various types of information, generally under a file folder format. For example, when a user creates a document with an application program, the document can be saved as a data file on the storage media. A request is sent from the application program to an operating system executing in the computer, and the operating system in turn sends a request to the storage device to store the data under a given specific file name. The storage device then stores the data as part of a file on the storage media at a specific location under the given specific file path (file name, folder names and file server name). The user may then, on a later stage, retrieve the data via the operating system and/or network file sharing/transmitting protocols; further manipulate the document, and then update/resave the data. However, in order to be able to retrieve the file, the user needs to decide on where to store the document and decide what name to give the document. Then, when the document needs to be retrieved, the user needs to recall where the document was stored and under what name it was stored. Consequently, when the number of files and folders increases, the user may experience difficulty in locating a specific document and may make several searching efforts in an attempt to locate the document.
As users amass more files, it becomes difficult to decide where and how to store the unstructured information. To illustrate, let's look at an instant where a father wishes to store a picture showing his daughter, Melody, in a recent ski trip to Lake Tahoe. Consider a folder tree shown in FIG. 1 is in a computer or in a photo application. In which folder should that particular photo be stored in order to facilitate easy retrieval at a later date? The picture shows Lake Tahoe, so maybe it should be stored under the “Lake Tahoe” folder 1020. On the other hand, there's a family member in the picture that was taken during a trip, so maybe it should be stored under “family album” folder 1040 or “2004 trips” folder 1050, and so on. Of course, a new folder, e.g., 1010, may also be created under a new name, say “Melody” (shown in broken line). If it is decided to create a new folder, then it is required to consider how to associate this new folder with existing tree folder structure and anticipate future files and folders. For example, “Melody” folder can be placed under “family album” to collect pictures of Melody, or it can be placed under the “2004 Trips” to collect all pictures of Melody taken during 2004 trips, or under “Ski Trip”, to collect all Melody's pictures taken during ski trips, etc. Furthermore, there may be a need to predict the folder structure accommodating future folders and future photos. For example, create “School” and “Birthday Party” folders in the future.
Thus, reviewing of the existing folder structure requires additional efforts and predicting folder structure for storing future information requires experience and foresight; it is tough to organize the information in a tree structure because information is often interrelated. Additionally, occasionally new data is received that may necessitate reorganizing the entire or part of the existing tree structure. This can be very disruptive. On the other hand, some newly received data simply doesn't fit into any of the existing folders and remains in a “loose” manner in some existing folder, which makes it very difficult to retrieve at a future date. In the end, despite having invested a lot of efforts to stay organized, the various files may still end up in a somewhat disorganized manner and provide a challenge trying to find certain information, as is currently experienced by even casual users of computers.
To address the issue of interrelationship between photos, some photo applications let the users to tag a photo with personal text strings. Tagging is like a virtual folder. In a tagging environment, the single version of a file can be simultaneously tagged by several tags. This function is equivalent to placing the file alias into multiple virtual folders without the need of duplicating the file. Therefore, this file is accessible within all the virtual folders where the alias resides. In other words, the tagging technology saves user's effort for creating a virtual folder named, creating the file alias and placing the alias inside the folder. The user simply points to the file and assigns a tag, and it is done. However, fundamentally it inherits the same issues as a tree structure. Tagging provides the user a more convenience way of implementing the old scheme. When implements too many tags, it becomes scattered like too many loose folders of very shallow depth.
A notebook that records information in a chronicle order is widely adopted to log events that arrive chronically. This form of information is useful for review of what has happened in the near term, follow-up, and to-do. It also servers handy for the user to temporary store the information and convert it to other forms when time is adequate. This type of information is rigidly organized by chronicle order, otherwise lacks of structure. It is important for the user to remember the approximate date when the information is recorded in order to retrieve the information.
Email system is a combination of all information platforms. On one hand it is similar to a notebook that by default, stores information in chronicle order. On the other hand it provides a simple database that let the users sort information by the sending/receiving party, date, and subject. In addition, it let users organize the information by folder tree structure and tags. Email system, however, merely gives its user to view the information in different information form. It still inherits the same problems from each individual platform. When organize emails in folders, it suffers the same issue of not addressing interrelated relationship. When view it in chronicle order, it lacks logical and human motive information.
Remote Access—Internet
Computers also generally access data stored on remote locations, such as via the Internet. Due to the proliferation of the Internet, an overwhelmingly large volume of information is available to computer users. However, finding, processing, organizing and storing the influx of this large volume of information is problematic. One approach is to store the information in a conventional database in a desired structure form where information is required to fit a set of prescribed parameters. For example, bank accounts websites are normally organized in a conventional database manner, so that information can be presented in the form of related tables (each similar to a spreadsheet). Another approach is to use a tree type structure. Retail merchant websites are normally organized in such a manner. For example, car-parts retail websites make the user follow a decision tree by asking the user to chose a model year, car maker, car model, etc, leading the user through a decision tree to the appropriate part for purchase.
Most of the web pages are interrelated by hyperlink. But from a user's view, much of the data available on the Internet is organized in a structure that is not always compatible with what they have in mind. In such a case, a conventional search engine may be used to search the files, or the user may find the files by following hyperlinks from other files. However, search tools tend to bring in large volume of information that requires the user to elaborately filter through them. If in the future the user wishes to return to a particular webpage, the user must recall the exact location of that webpage or the user must bookmark it when first viewing it in order to be able to use the bookmark for a return visit. Still, because of the large number of web pages available on the Internet, if the user bookmarks every page the user may wish to return in the future, the user will find it difficult to find a particular bookmark among the large number of bookmarks created. Consequently, users find it necessary to create a filing scheme even for bookmarks, which results in the same problem as with saving a document, i.e., in which folder to store a bookmark and under what name. For instance, Yahoo!Bookmarks uses a folder structure (1H) to organize their subscribers' personal web bookmarks. Some websites like Amazon.com have a “wish list” mechanism that allows their customers to store their favorite merchandise items on site. This mechanism has a few drawbacks from the consumers' view: (1) When the wish list grows, it becomes hard to manage and (2) when a purchase is to be made, it is difficult to compare related merchandise items stored in different websites.
Furthermore, conventional searching, using search engines such as Google.com, Yahoo.com and the likes, is not very efficient due to the large volume of returned results which has very little relevance to the subject matter sought after. That is, in order to perform an efficient search, the user needs to become proficient and structuring efficient searches. Looking at a casual user, for example, assume the user would like to purchase a sweater. If a user simply enters the term “sweater” on a conventional search engine, more than a million matches for “sweater” keyword would be returned. Consequently, the user needs to invest efforts to further narrow down the search result to, perhaps, less than a hundred. Understanding this problem, certain Internet providers, such as Info.com for example, try to help the user brain-storm by providing suggestions for the search terms; however, the suggestions are often not related to what the user is searching. For instance, for “sweater”, it suggests “jacket”, “Bathrobe”, “Shirt”, etc. to the user, which are not relevant to a user looking to find a sweater for purchase.
Libraries—Loose Data
Another utility made available by the advent of computers and the Internet is the ability to make collections of information, such as electronic libraries. For example, many publishers have their printed collections also available electronically online. Similarly, PC users may store their own collection of data files, e.g., articles, recipes, letters, etc. Again, in order to facilitate retrieval of the stored information from such collections, a storage scheme needs to be developed. So, for example, publishers of journals may utilize publication date storage scheme, where each volume is stored under its publication date. On the other hand, many journals have the journals broken down to articles written by different departments, in which case, the various articles will be saved under the department's subject matter—separately from the particular issue date of the journal. Therefore, in order to retrieve a particular article, the user needs to understand the storage structure and have some knowledge about the article, e.g., on what date it was published? In what journal volume? Under which department? Etc.
A similar problem exists when PC users try to create their own collection of data on their own system. Consider for example an engineer who collects published technical articles. These can be stored according to subject matter, according to the name of the journal, according to the author, according to publication date, according to the conference in which the paper was published, and so on. The user has to select a storage strategy at the onset, and then have some information about each article in order to be able to retrieve it. So, for example, assuming that the user chose to store the articles under subject matter. The user at a later time tries to retrieve an article of which he knows the author and the conference in which the author presented the paper, but he is unsure of the exact subject matter. This will present a challenge to the user to retrieve the article—the user will have to either comb through articles of various subject matters, or perform a global search and hope that the number of returned results is not too large.
Moreover, the engineers after reviewing the information may gain some thoughts regarding the technical articles. These thoughts can be related to tasks or projects. These thoughts are often written in a notebook for future reference. Since this type of events come random and out of synchronization in life, its relevancy to the tasks and projects may not be clear to the engineer at the point of conception. While the task or project has their own set of objectives, deliveries, milestones, deadlines, etc. It is very difficult for the engineers to keep track and integrate their accumulated thoughts to their tasks or projects. Similar issues for project managers who receive unstructured information (emails, reports, customers feedbacks, business activities/events) daily and try to tie the information with their project objectives, tasks, milestones, deadlines, etc.
Human Motives
To make information interesting, meaningful, and valuable to the user, it has to integrate the user's personal motives, such as but not limited to thoughts, meanings, preferences, priorities, objectives, goals, project structures, planning ideas, intentions, wishes, action items, decisions, conclusions, moods, motivations, processing sequences, questions, etc. A digital content may not contain the user's personal motives, for instance, a public web page, someone else's emails or documents, merchandise information, commercial music and videos, photos, books, etc. The user's motives are often personal, diversified, unstructured. To reflect the user's personal motives, the user may constantly rewrite the motives in documents, in emails, in the digital content name and folder structures, data in an application, paper notebook, yellow stickers, napkins, or loose papers. When the project makes progress and evolves, much of the information becomes obsolete and irrelevant to the present date motives. When the motives reflect in folder structures, it works best when used for a hierarchical tree structure. This may in conflicts with personal motives that tent to be interrelated and not structured. During the early development of human motives, it is often not well thought out. It is premature for the user to forecast a well thought out tree structure. Once the folder structure is articulated, other digital content are filled into the folders and become inflexible to be updated. The structure becomes inadequate when the motives and the collection of digital data evolve.
Conventional (desktop or web) search engines are often brought in to search the digital content and motives that have become scattered and massy. But it is ineffective and time consuming to achieve the objectives of this invention. The issues are that the user needs to intelligently drive the search engine in order to retrieve relevant and meaningful motives. Search engine can bring in information containing the keywords. But many of the relevant information does not containing the motive keywords. For instance, the vendor's standard part specification may be used for a specific engineering task but it is not mentioned in the document because the specification is for general purpose. Because many digital content does not contain the user's personal meanings, the user often do not remember what keywords to use to retrieve the data. Search engine often brings in too many irrelevant or obsolete data; it cannot help to construct the structure and interrelationship between motives and digital content. It relies on the user to have the pre-knowledge of this interrelationship and painstakingly review pieces of search results and manually construct the information structure.
Some application like Microsoft Project tie deliveries, and tasks/to-do, milestones together with resources and time for a project. It provides an effective mean for drawing interdependency between tasks, deadlines/milestones and available resources. However, it is limited when used to address complex interrelationship between tasks, to-do (human motives) and digital content. For instance, an engineering task may need to consider the marketing requirements from several customers, preliminary experimental data and research reports from literatures. A marketing milestone may involve feeding back engineering progress to the customers and it may involve multiple emails exchange with the engineering departments, business decision makers, and several customers. A manufacture task may require an engineering change order (ECO) and multiple follow up with vendors, while an engineering task may be related to multiple emails exchange with manufacture engineers who express concerns on phasing out existing inventory parts, availability of the parts and tight schedule. The complexity glows with the size of the projects and the number of people/departments involved in the project.
Prior Art Interrelated/Crosslink Data Structure
The complex cross relationship between data has been used in several applications to address none hierarchical (tree) type data structure. ThinkPad.com's technology was utilized by Visualthesaurus.com to express the complex relationship between related words and topics (a multiple word text string). For instance, when one looks up keyword “legal” (101B, FIG. 1B), it shows related words like “law”, “illegal”, “judicial”, etc. When the user clicks on the keyword “judicial” (102B), the system shows related words like “justice”, “judge”, “discriminative”, etc (FIG. 1C). When a keyword is inquired, the dictionary meaning of the keyword is also shown (102D, FIG. 1D). TheBrain.com employs a similar technology for a user to manage their complex interrelated digital content and thoughts. Topics and digital content can be cross-linked. Like the dictionary words, when inquire about a topic (101F, FIG. 1F), it shows the related topics (102F) and digital content (103F) in a visual map. When clicks on a content member, the details of the data content is displayed. Both companies demonstrate the applications of a none-hierarchical data structure and present this structure using a visual map. To avoid cluttering the visual map, related topics and digital content needs to be group into a layer tree branches. So the user has a limited vision of the branch; in contrast, the user of a tree structure can see how the current folder (topic equivalent) branches off from the main. Therefore, this technology gains flexibility to be able to interrelate digital content and motives, but at the expense of seeing the main data structure.
Many issues need to be addressed if this data structure is to be adopted to accomplish such an objective. First of all, it may create multiple similar topics or keywords that are difficult for the user to keep track of. When type in a topic, it suggests other topics that share the same first word. If a user first enters a second word of a topic, this topic will not be suggested. In addition, topics by itself lacks context. Unlike a folder in a tree structure, the user may grasp the meaning of a folder with the help of the folder tree. Try to stay on the track of thought or gain an integrative view of the information would have been much more difficult when compares to a tree structure. A user may easily get lost after following a few leads in this type of data structure. An example is shown in FIG. 1E when “true” is inquired. Moreover, it lacks of priority or weight between related elements because of no hierarchy. When compares to a tree data structure, it suffers many short comings in exchange for the ability to crosslink.
Similar data structure as that used by Visualthesaurus called tagging has been widely adopted for organizing web content but expressed in words by the search engine info.com or for web blogs technorati.com. For instance, when one searches for keyword “grill” in info.com it suggests related topics like gas grills, barbeque grills, grilling BBQ, Weber grill, grilling recipes, barbecue, charcoal grills and outdoor gas grill. In this case, the keyword “grill” is connecting to the above topics in a similar way that “legal” connects to other related keywords in FIG. 1B. It is not clear whether the data structure allows the user to compose this relationship. Nevertheless, it inherits the same problem as that of the Visualthesaurus data structure. Other examples are web site like del.icio.us where a group of related “tags” is presented when a tag is inquired. Each tag may be a plurality of words just like that in Visulathesaurus. For instance, when one selects the tag “tech,” it returns “blog”, “technology”, “news, web”, “software”, “geek”, “tools”, “reference”, “programming”, “blogs”, and “daily”. Similarly, web site 43things.com relates activities (each described by a topic) together like the dictionary words in Visualthesaurus. To emphasize a topic, it increases its font size, for instance, see 101E, FIG. 1E. Flikr.com uses similar scheme for their users to relate a topic to a group of photos. Google “GMail” and Apple “Mail” let their users to relate a topic (tag) with a group of emails.
The tagging technology and its limitation are summarized in an example as shown in FIGS. 45A and 45B. Tagging technology allows a file to be associating with a plurality of tags: File 1 is associating with ice cream and recipe via the “connections” 4510, and 4520; file 2 is associating recipe, sushi and Japanese cooking via the “connections” 4530, 4540 and 4550; file 3 is associating with recipe and chemical database via the “connections” of 4560 and 4570. On the other hand, the recipe tag 4500 is associating with file 1, file 2 and file 3 via the “connections” of 4520, 4530 and 4560. The recipe tag is also associating with other tags that are associating with these three files via the associations with file 1, file 3 and file 3: ice cream, sushi, Japanese cooking, and chemical database. In this example, only three files are involved. But it may have confused the user why ice cream and chemical database are related at all. This drawbacks may be seen in del.ico.us, 43things.com, and flickr.com where webpage bookmarks, blogs, or photos are tagged, when the number of digital contents are involved, the association of tags are in large number and often are confusing and incomprehensible. The other issue with tagging is that it allows one to inquire about one digital content or one tag in a time. It does not allow progressively narrowing the tags like following a tree structure. These two drawbacks are the main flaw that prevent tagging technology to be applied in applications that require organizing complex structure of information that currently dominated by tree structures and conventional database structures.
The simplified version of how tagging technology is implemented is described next. The database table 4510B contains a plurality of records; each record represents a “connection” between the files and the tags in FIG. 45A. For instance, record n1 represents the connection 4510 between File 1 and ice cream. To find the tags associating with File 1, a filter is applied to find any records that containing the File 1 file; in this case records n1 and n2, therefore, the tags of ice cream and recipe. On the other hand, to find the files associating with the tag recipe, a filter is applied to find any records containing the recipe tag; in this case, records n2, n3, n6, therefore, file 1, file 2 and file 3 are retrieved; name the outcome as table A. To find the association of other tags with the recipe tag, retrieve any records that containing in table A: file 1, file 2 and file 3. This step is a common operation in database by joint table between the file data field of table A and the file data field of original table.
FIG. 44 briefly summarizes the deficiency of all existing systems and methods for integrating digital content and motives that store in different platforms:
Search Engines:
                1. Deliver an overwhelming amount of data that is often overbroad and irrelevant.        2. Lack organization structure. The user lacks a grand plot of thought; tend to narrowly focus on each individual information when it is needed.        3. Cannot find information that you completely do not remember. When things are organized, we can be reminded of important information that escapes our mind. Since organization is not part of the search engine, it relies on the user to initiate a search. One cannot initiate a search if not aware of the proper keywordsHyperlink/Prior Art Crosslink/Tagging Data Structure:        1. When compares to a tree structure, it is unable to clearly present a clear relationship for a complex data structure. The strength of hyperlink and tagging provide a user friendly reference to other information.        2. Quickly loss track of thought when jump between hyperlinks and tags. Because this platform lacks clear structure, it relies on the user to draw the overall information structure by reviewing related information. But when information is cross-referenced, one can quickly lose track.        3. When compares to a tree structure, it lacks priority or weight between related elements in the structure.File Folders/Tree Structure:        1. Cannot organize interrelated information. Tree structure has a rigid hierarchy. When information is interrelated, hierarchy logic breaks down.        2. The rigid hierarchy makes it impossible to view information from a different perspective.        3. In creating a tree folder structure, one must predict future files and future requirements based on today's perspective, which makes it very difficult to construct the structure and leads to revisions and inefficiencies.        4. Re-structuring of a tree is time consuming and often disruptive, especially if various files emails or web pages contain hyperlinks to other files. These hyperlinks must also be changed to reflect the revise structure, or they will not function properly. It is impossible to keep tracks of these hyperlinks that imbedded in files and web pages.Conventional Databases:        1. All input information must be structured according to the rigid requirements (format) of the database. It is not suitable to handle diversified information and information that evolves and needs constant restructuring.        2. Creation of a conventional database requires high level of technical skill and familiarity with the architecture of the database software.Chronicle Logger (Notebook/Conventional Emails):        1. Poor data structure. Notebook stores information in a rigid chronicle sequence.        2. Time consuming to gather information that scatters in the notebook/emails over a period of time when information becomes versatile.        
Thus, there is a continuing and growing need for systems and methods for organizing digital content and motives in a meaningful, integral, and timely manner that allows the scope of content, motives and available information to be quickly ascertained and that enables quick access to information of particular interest.